


"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class    __a						:







						@staticmethod
						def      lowerCamelCase_     ( *UpperCAmelCase      ,	**UpperCAmelCase   ):

							'''simple docstring'''






							pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    __a						( unittest.TestCase							):
				lowerCamelCase     :       List[Any]			       =MODEL_FOR_OBJECT_DETECTION_MAPPING







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			ObjectDetectionPipeline(model=UpperCAmelCase      ,	image_processor=UpperCAmelCase   )
					return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      ,	threshold=0.0   )

					self.assertGreater(len(UpperCAmelCase   )      ,	0   )
					for detected_object in outputs:
						self.assertEqual(
						    UpperCAmelCase      ,	{
						        '''score''': ANY(UpperCAmelCase   ),
						        '''label''': ANY(UpperCAmelCase   ),
						        '''box''': {'''xmin''': ANY(UpperCAmelCase   ), '''ymin''': ANY(UpperCAmelCase   ), '''xmax''': ANY(UpperCAmelCase   ), '''ymax''': ANY(UpperCAmelCase   )},
						    }      ,	)

					import datasets

					lowerCAmelCase_      							=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,	'''image'''      ,	split='''test'''   )

					lowerCAmelCase_      							=			[
					    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   ),
					    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    # RGBA
					    dataset[0]['''file'''],
					    # LA
					    dataset[1]['''file'''],
					    # L
					    dataset[2]['''file'''],
					]
					lowerCAmelCase_      							=			object_detector(UpperCAmelCase      ,	threshold=0.0   )

					self.assertEqual(len(UpperCAmelCase   )      ,	len(UpperCAmelCase   )   )
					for outputs in batch_outputs:
						self.assertGreater(len(UpperCAmelCase   )      ,	0   )
						for detected_object in outputs:
							self.assertEqual(
							    UpperCAmelCase      ,	{
							        '''score''': ANY(UpperCAmelCase   ),
							        '''label''': ANY(UpperCAmelCase   ),
							        '''box''': {'''xmin''': ANY(UpperCAmelCase   ), '''ymin''': ANY(UpperCAmelCase   ), '''xmax''': ANY(UpperCAmelCase   ), '''ymax''': ANY(UpperCAmelCase   )},
							    }      ,	)







				@require_tf
				@unittest.skip('''Object detection not implemented in TF'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass







				@require_torch
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''hf-internal-testing/tiny-detr-mobilenetsv3'''

					lowerCAmelCase_      							=			AutoModelForObjectDetection.from_pretrained(UpperCAmelCase   )
					lowerCAmelCase_      							=			AutoFeatureExtractor.from_pretrained(UpperCAmelCase   )
					lowerCAmelCase_      							=			ObjectDetectionPipeline(model=UpperCAmelCase      ,	feature_extractor=UpperCAmelCase   )

					lowerCAmelCase_      							=			object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''      ,	threshold=0.0   )

					self.assertEqual(
					    nested_simplify(UpperCAmelCase      ,	decimals=4   )      ,	[
					        {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
					        {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
					    ]      ,	)

					lowerCAmelCase_      							=			object_detector(
					    [
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    ]      ,	threshold=0.0      ,	)

					self.assertEqual(
					    nested_simplify(UpperCAmelCase      ,	decimals=4   )      ,	[
					        [
					            {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
					            {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
					        ],
					        [
					            {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
					            {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
					        ],
					    ]      ,	)







				@require_torch
				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''facebook/detr-resnet-50'''

					lowerCAmelCase_      							=			AutoModelForObjectDetection.from_pretrained(UpperCAmelCase   )
					lowerCAmelCase_      							=			AutoFeatureExtractor.from_pretrained(UpperCAmelCase   )
					lowerCAmelCase_      							=			ObjectDetectionPipeline(model=UpperCAmelCase      ,	feature_extractor=UpperCAmelCase   )

					lowerCAmelCase_      							=			object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''   )
					self.assertEqual(
					    nested_simplify(UpperCAmelCase      ,	decimals=4   )      ,	[
					        {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
					        {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
					        {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
					        {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
					        {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
					    ]      ,	)

					lowerCAmelCase_      							=			object_detector(
					    [
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    ]   )
					self.assertEqual(
					    nested_simplify(UpperCAmelCase      ,	decimals=4   )      ,	[
					        [
					            {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
					            {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
					            {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
					            {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
					            {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
					        ],
					        [
					            {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
					            {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
					            {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
					            {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
					            {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
					        ],
					    ]      ,	)







				@require_torch
				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''facebook/detr-resnet-50'''

					lowerCAmelCase_      							=			pipeline('''object-detection'''      ,	model=UpperCAmelCase   )

					lowerCAmelCase_      							=			object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''   )
					self.assertEqual(
					    nested_simplify(UpperCAmelCase      ,	decimals=4   )      ,	[
					        {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
					        {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
					        {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
					        {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
					        {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
					    ]      ,	)

					lowerCAmelCase_      							=			object_detector(
					    [
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    ]   )
					self.assertEqual(
					    nested_simplify(UpperCAmelCase      ,	decimals=4   )      ,	[
					        [
					            {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
					            {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
					            {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
					            {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
					            {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
					        ],
					        [
					            {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
					            {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
					            {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
					            {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
					            {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
					        ],
					    ]      ,	)







				@require_torch
				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			0.9_9_8_5
					lowerCAmelCase_      							=			'''facebook/detr-resnet-50'''

					lowerCAmelCase_      							=			pipeline('''object-detection'''      ,	model=UpperCAmelCase   )

					lowerCAmelCase_      							=			object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''      ,	threshold=UpperCAmelCase   )
					self.assertEqual(
					    nested_simplify(UpperCAmelCase      ,	decimals=4   )      ,	[
					        {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
					        {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
					    ]      ,	)







				@require_torch
				@require_pytesseract
				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''Narsil/layoutlmv3-finetuned-funsd'''
					lowerCAmelCase_      							=			0.9_9_9_3

					lowerCAmelCase_      							=			pipeline('''object-detection'''      ,	model=UpperCAmelCase      ,	threshold=UpperCAmelCase   )

					lowerCAmelCase_      							=			object_detector(
					    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''   )
					self.assertEqual(
					    nested_simplify(UpperCAmelCase      ,	decimals=4   )      ,	[
					        {'''score''': 0.9_9_9_3, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
					        {'''score''': 0.9_9_9_3, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
					    ]      ,	)



"""simple docstring"""



import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase_							=							16
lowercase_							=							32
def      UpperCAmelCase     (       _lowercase :   Accelerator			,   _lowercase :   int = 1_6			,   _lowercase :   str = "bert-base-cased"	)						-> Dict:


	"""simple docstring"""




	lowerCAmelCase_      							=			AutoTokenizer.from_pretrained(_lowercase	)
	lowerCAmelCase_      							=			load_dataset('''glue'''			,   '''mrpc'''	)

	def tokenize_function(_lowercase :   Optional[int]	):
		# max_length=None => use the model max length (it's actually the default)
		lowerCAmelCase_      							=			tokenizer(examples['''sentence1''']			,   examples['''sentence2''']			,   truncation=_lowercase			,   max_length=_lowercase	)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	lowerCAmelCase_      							=			datasets.map(
	    _lowercase			,   batched=_lowercase			,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,   load_from_cache_file=_lowercase	)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	lowerCAmelCase_      							=			tokenized_datasets.rename_column('''label'''			,   '''labels'''	)

	def collate_fn(_lowercase :   Any	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_lowercase			,   padding='''max_length'''			,   max_length=1_2_8			,   return_tensors='''pt'''	)
		return tokenizer.pad(_lowercase			,   padding='''longest'''			,   return_tensors='''pt'''	)

	# Instantiate dataloaders.
	lowerCAmelCase_      							=			DataLoader(
	    tokenized_datasets['''train''']			,   shuffle=_lowercase			,   collate_fn=_lowercase			,   batch_size=_lowercase	)
	lowerCAmelCase_      							=			DataLoader(
	    tokenized_datasets['''validation''']			,   shuffle=_lowercase			,   collate_fn=_lowercase			,   batch_size=_lowercase	)

	return train_dataloader, eval_dataloader
def      UpperCAmelCase     (       _lowercase :   int			,   _lowercase :   Any			,   _lowercase :   Any			,   _lowercase :   str	)						-> List[Any]:


	"""simple docstring"""




	model.eval()
	lowerCAmelCase_      							=			0
	for step, batch in enumerate(_lowercase	):
		# We could avoid this line since we set the accelerator with `device_placement=True`.
		batch.to(accelerator.device	)
		with torch.no_grad():
			lowerCAmelCase_      							=			model(**_lowercase	)
		lowerCAmelCase_      							=			outputs.logits.argmax(dim=-1	)
		# It is slightly faster to call this once, than multiple times
		lowerCAmelCase_							,	lowerCAmelCase_      							=			accelerator.gather(
		    (predictions, batch['''labels'''])	)  # If we are in a multiprocess environment, the last batch has duplicates
		if accelerator.use_distributed:
			if step == len(_lowercase	) - 1:
				lowerCAmelCase_      							=			predictions[: len(eval_dataloader.dataset	) - samples_seen]
				lowerCAmelCase_      							=			references[: len(eval_dataloader.dataset	) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=_lowercase			,   references=_lowercase			,   )

	lowerCAmelCase_      							=			metric.compute()
	return eval_metric["accuracy"]
def      UpperCAmelCase     (       _lowercase :   Dict			,   _lowercase :   Optional[Any]	)						-> Dict:


	"""simple docstring"""




	lowerCAmelCase_      							=			Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCAmelCase_      							=			config['''lr''']
	lowerCAmelCase_      							=			int(config['''num_epochs''']	)
	lowerCAmelCase_      							=			int(config['''seed''']	)
	lowerCAmelCase_      							=			int(config['''batch_size''']	)
	lowerCAmelCase_      							=			args.model_name_or_path

	set_seed(_lowercase	)
	lowerCAmelCase_							,	lowerCAmelCase_      							=			get_dataloaders(_lowercase			,   _lowercase			,   _lowercase	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCAmelCase_      							=			AutoModelForSequenceClassification.from_pretrained(_lowercase			,   return_dict=_lowercase	)

	# Instantiate optimizer
	lowerCAmelCase_      							=			(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	lowerCAmelCase_      							=			optimizer_cls(params=model.parameters()			,   lr=_lowercase	)

	if accelerator.state.deepspeed_plugin is not None:
		lowerCAmelCase_      							=			accelerator.state.deepspeed_plugin.deepspeed_config[
		    '''gradient_accumulation_steps'''
		]
	else:
		lowerCAmelCase_      							=			1
	lowerCAmelCase_      							=			(len(_lowercase	) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		lowerCAmelCase_      							=			get_linear_schedule_with_warmup(
		    optimizer=_lowercase			,   num_warmup_steps=0			,   num_training_steps=_lowercase			,   )
	else:
		lowerCAmelCase_      							=			DummyScheduler(_lowercase			,   total_num_steps=_lowercase			,   warmup_num_steps=0	)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_      							=			accelerator.prepare(
	    _lowercase			,   _lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)

	# We need to keep track of how many total steps we have iterated over
	lowerCAmelCase_      							=			0
	# We also need to keep track of the stating epoch so files are named properly
	lowerCAmelCase_      							=			0
	lowerCAmelCase_      							=			evaluate.load('''glue'''			,   '''mrpc'''	)
	lowerCAmelCase_      							=			num_epochs

	if args.partial_train_epoch is not None:
		lowerCAmelCase_      							=			args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint	)
		lowerCAmelCase_      							=			args.resume_from_checkpoint.split('''epoch_'''	)[1]
		lowerCAmelCase_      							=			''''''
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		lowerCAmelCase_      							=			int(_lowercase	) + 1
		lowerCAmelCase_      							=			evaluation_loop(_lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)
		accelerator.print('''resumed checkpoint performance:'''			,   _lowercase	)
		accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''			,   lr_scheduler.get_lr()[0]	)
		accelerator.print('''resumed optimizers\'s lr:'''			,   optimizer.param_groups[0]['''lr''']	)
		with open(os.path.join(args.output_dir			,   F"""state_{starting_epoch-1}.json"""	)			,   '''r'''	) as f:
			lowerCAmelCase_      							=			json.load(_lowercase	)
			assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["lr"] == lr_scheduler.get_lr()[0]
			), "Scheduler learning rate mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
			), "Optimizer learning rate mismatch, loading from checkpoint failed"
			assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
			return

    # Now we train the model
	lowerCAmelCase_      							=			{}
	for epoch in range(_lowercase			,   _lowercase	):
		model.train()
		for step, batch in enumerate(_lowercase	):
			lowerCAmelCase_      							=			model(**_lowercase	)
			lowerCAmelCase_      							=			outputs.loss
			lowerCAmelCase_      							=			loss / gradient_accumulation_steps
			accelerator.backward(_lowercase	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		lowerCAmelCase_      							=			F"""epoch_{epoch}"""
		lowerCAmelCase_      							=			os.path.join(args.output_dir			,   _lowercase	)
		accelerator.save_state(_lowercase	)
		lowerCAmelCase_      							=			evaluation_loop(_lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)
		lowerCAmelCase_      							=			accuracy
		lowerCAmelCase_      							=			lr_scheduler.get_lr()[0]
		lowerCAmelCase_      							=			optimizer.param_groups[0]['''lr''']
		lowerCAmelCase_      							=			epoch
		lowerCAmelCase_      							=			overall_step
		accelerator.print(F"""epoch {epoch}:"""			,   _lowercase	)

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			with open(os.path.join(args.output_dir			,   F"""state_{epoch}.json"""	)			,   '''w'''	) as f:
				json.dump(_lowercase			,   _lowercase	)
def      UpperCAmelCase     (       )						-> Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
	parser.add_argument(
	    '''--model_name_or_path'''			,   type=_lowercase			,   default='''bert-base-cased'''			,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,   required=_lowercase			,   )
	parser.add_argument(
	    '''--output_dir'''			,   type=_lowercase			,   default='''.'''			,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,   )
	parser.add_argument(
	    '''--resume_from_checkpoint'''			,   type=_lowercase			,   default=_lowercase			,   help='''If the training should continue from a checkpoint folder.'''			,   )
	parser.add_argument(
	    '''--partial_train_epoch'''			,   type=_lowercase			,   default=_lowercase			,   help='''If passed, the training will stop after this number of epochs.'''			,   )
	parser.add_argument(
	    '''--num_epochs'''			,   type=_lowercase			,   default=2			,   help='''Number of train epochs.'''			,   )
	lowerCAmelCase_      							=			parser.parse_args()
	lowerCAmelCase_      							=			{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}

	training_function(_lowercase			,   _lowercase	)


if __name__ == "__main__":
		main()